As digital imaging becomes more commonplace, improving image quality becomes a central goal of many image processing applications. Many of the existing applications utilize the data within a digital image to extrapolate more pixels, thereby adding more detail to the image at hand (also known as super-resolution).
Most super-resolution techniques require multiple low resolution images to be aligned in sub-pixel accuracy. Image super-resolution from a single image, however, introduces a more complicated problem. More specifically, single image super-resolution is an under-constrained problem because many high resolution images can produce the same low resolution image.
Current solutions for single image super-resolution include functional interpolation and reconstruction-based ones. Functional interpolation methods often blur the discontinuities and do not satisfy the reconstruction constraint. Under the reconstruct constraint, the down-sampled high resolution reconstructed image should be as close as possible to the original low resolution image.
FIG. 1 illustrates a sample image utilized to illustrate the shortcomings of the prior art. A portion of FIG. 1 (102) will be utilized as a reference image for comparison purposes. FIGS. 2 and 3 show the results of nearest neighbor interpolation (simple copying of neighboring pixels) and bicubic interpolation (functional interpolation) of a low resolution image, respectively.
Reconstruction-based methods satisfy the reconstruction constraint but cannot guarantee contour smoothness. FIG. 4 shows the result of a reconstruction-based approach using backprojection. As illustrated, some “jaggy” and “ringing” artifacts are clearly visible along the edges.
Accordingly, the present solutions fail to provide single image super-resolution without visible artifacts.